Exploring first-year engineering students’ learning strategies and academic performance
Notice bibliographique
Résumé
This study investigated the learning strategies of 450 U.S. engineering freshmen and their academic performance. Paired-samples t-tests indicated significant improvements in learning strategies, with higher mean scores on the post-survey compared to the pre-survey, except for the attitude subscale. Variation in two subscales, selecting main ideas and test strategies, was observed among demographic groups. Pell, first-generation, racially minoritized, and female students initially reported lower levels of learning strategies, but these differences diminished in the post-survey. Hierarchical linear regression analyses revealed learning strategies related to (coping with) anxiety and motivation significantly predicted academic performance, with effective anxiety management and higher motivation scores associated with better academic performance. This study provides insights into the learning strategies employed by first-year engineering students and their relationship with academic performance. It highlights the potential for improvements in these strategies over time and how they vary among different demographic groups. Cited as: Zhang, S., Shi, Q., Garza, T., Li, C. (2024). Exploring first-year engineering students’ learning strategies and academic performance. Education and Lifelong Development Research, 1(2), 58-71. https://doi.org/10.46690/elder.2024.02.02 References: Alzubaidi, E., Aldridge, J. M., & Khine, M. S. (2016). Learning English as a second language at the university level in Jordan: Motivation, self-regulation and learning environment perceptions. Learning Environments Research, 19(1), 133–152. American Society for Engineering Education. (2016). Engineering by the numbers: ASEE retention and time-to-graduation benchmarks for undergraduate engineering schools, departments and programs. Washington, D.C. 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Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».